Other models use Multi-Task Learning (MTL) based on the idea that simultaneously learning related tasks can improve the performance on all of them [1]}, [2]}, [3]}.
Hence, related tasks facilitate the transfer of shared knowledge among them.
E.g., edge detection improves segmentation by adjusting the edge at each level of a CNN [4]}, [5]}, [6]} or by learning edge-aware features [7]}.
Although several MTL approaches [8]}, [9]}, [10]}, [11]} were applied to the SS task, it is still difficult to say which auxiliary tasks most beneficial for the final SS results.
Even more, which of these auxiliary tasks provide the necessary complementary information so that the models can address the loss of spatial precision.
We deduce that by reinforcing the object contours' information (through auxiliary tasks), we will be able to force greater attention (in the training phase) on the segmented objects' contours through the MTL approach.
